Human body target detection and sexuality recognition method in video monitoring

Abstract

The invention discloses a detection and gender identification method of a human body object in video supervision, which comprises: (a) establishing a column diagram of foreground skin and background in an HSV color space according to a collected image database; (b) utilizing the Bayes classifier to process foreground and background judgement for each pixel point of an input video, so as to cut out a human body skin region in the video; (c) utilizing biological specificity of human body skin-color to cut the foreground skin and the background again in a complex color space (E, R / G, H) so as to eliminate interference of the objects with similar skin-color in the background to obtain an accurate human body object; (d) utilizing a mode identification method according to the face image in the obtained human body object to process gender identification. The invention takes the feature of face skin of the human body as a main mark of the human body and detects the human body object in the video image so as to overcome the adverse effect caused by illumination changes and the interference of similar color in the background, and improves correct identification of the gender of the object.

Description

technical field

[0001] The invention belongs to the technical field of image information processing, and in particular relates to a technology for automatically detecting human faces in video surveillance images and using a pattern recognition method to identify the gender of the detected human faces. Background technique

[0002] Face is an important biological characteristic of human beings, which can reflect important information such as a person's identity, age, emotion and gender. Gender classification has received extensive attention in the fields of psychology and machine vision. Most of the early computer vision gender classifications were machine learning methods based on neural networks, including the two-layer neural network SEXNET of Gollomb et al. Gender discrimination was carried out on the pictures, and the correct gender recognition rate reached 91.9% in 90 pictures (45 men and women each); Edelman et al. used three different parts of the face (the whole face...

Examples

Embodiment 1

[0029] Embodiment 1. In this embodiment, a video surveillance system is used to obtain continuous video image sequences to establish an image database, and then calls such as figure 1 The human target detection and gender recognition method shown is used to obtain the gender of the target to be detected. The specific execution steps of the human object detection and gender recognition method are described in detail below.

[0030] The first step is to read a frame of image data in the video image sequence. In this embodiment, in order to ensure that the detection program can run cyclically, it is defined here as reading the next frame of image data.

[0031] The second step is to use Bayesian classifier for skin color detection.

[0032] The principle of Bayesian classification and detection of human skin color based on pixel level is to calculate the conditional probability of a pixel belonging to the skin color area and the non-skin color area according to the color histog...

technical field

[0001] The invention belongs to the technical field of image information processing, and in particular relates to a technology for automatically detecting human faces in video surveillance images and using a pattern recognition method to identify the gender of the detected human faces. Background technique

[0002] Face is an important biological characteristic of human beings, which can reflect important information such as a person's identity, age, emotion and gender. Gender classification has received extensive attention in the fields of psychology and machine vision. Most of the early computer vision gender classifications were machine learning methods based on neural networks, including the two-layer neural network SEXNET of Gollomb et al. Gender discrimination was carried out on the pictures, and the correct gender recognition rate reached 91.9% in 90 pictures (45 men and women each); Edelman et al. used three different parts of the face (the whole face...

Examples

Embodiment 1

[0029] Embodiment 1. In this embodiment, a video surveillance system is used to obtain continuous video image sequences to establish an image database, and then calls such as figure 1 The human target detection and gender recognition method shown is used to obtain the gender of the target to be detected. The specific execution steps of the human object detection and gender recognition method are described in detail below.

[0030] The first step is to read a frame of image data in the video image sequence. In this embodiment, in order to ensure that the detection program can run cyclically, it is defined here as reading the next frame of image data.

[0031] The second step is to use Bayesian classifier for skin color detection.

[0032] The principle of Bayesian classification and detection of human skin color based on pixel level is to calculate the conditional probability of a pixel belonging to the skin color area and the non-skin color area according to the color histog...

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